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关 键 词:stata导出excel
行 业:IT 软件 翻译软件
发布时间:2021-08-28
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讲座主题:因果推断: 内生性问题与工具变量法
讲座时间:5月19日15:00-16:00
讲座概述:因果推断模型与方法是现代社会科学研究者利用计量模型发表高水平实证论文的核心利器。其中,工具变量法则举足轻重,是计量研究中无法避开的挑战之一。此专题就内生性问题与解决路径、工具变量选择的方法及分类、工具变量的检验及工具变量回归模型(2SLS+GMM)等进行专题讨论,探讨工具变量法的基本思想、原理、模型、方法及适用范围,并以真实数据为演示案例,分享因果推断的思辨、工具变量回归模型的构建、应用及结果解读的路径,为发表高水平的实证研究论文奠定基础。
anyvalue(), anymatch(), and anycount() are for categorical or other variables taking integer
values. If we define a subset of values specified by an integer numlist (see [U] 11.1.8 numlist),
anyvalue() extracts the subset, leaving every other value missing; anymatch() defines an indicator
variable (1 if in subset, 0 otherwise); and anycount() counts occurrences of the subset across a set
of variables. Therefore, with one variable, anymatch(varname) and anycount(varname) are
equivalent.
With the auto dataset, we can generate a variable containing the high values of rep78 and a
variable indicating whether rep78 has a high value:
Frequentist inference is based on the sampling distributions of estimators of parameters and provides
parameter point estimates and their standard errors as well as confidence intervals. The exact sampling
distributions are rarely known and are often approximated by a large-sample normal distribution.
Bayesian inference is based on the posterior distribution of the parameters and provides summaries of
this distribution including posterior means and their MCMC standard errors (MCSE) as well as credible
intervals. Although exact posterior distributions are known only in a number of cases, general posterior
distributions can be estimated via, for example, Markov chain Monte Carlo (MCMC) sampling without
any large-sample approximation.
Frequentist confidence intervals do not have straightforward probabilistic interpretations as do
Bayesian credible intervals. For example, the interpretation of a 95% confidence interval is that if
we repeat the same experiment many times and compute confidence intervals for each experiment,
then 95% of those intervals will contain the true value of the parameter. For any given confidence
interval, the probability that the true value is in that interval is either zero or one, and we do not
know which. We may only infer that any given confidence interval provides a plausible range for the
true value of the parameter. A 95% Bayesian credible interval, on the other hand, provides a range
for a parameter such that the probability that the parameter lies in that range is 95%.
generate’s sum() function creates the vertical, running sum of its argument, whereas egen’s
total() function creates a constant equal to the overall sum. egen’s rowtotal() function, however,
creates the horizontal sum of its arguments. They all treat missing as zero. However, if the missing
option is specified with total() or rowtotal(), then newvar will contain missing values if all
values of exp or varlist are missing.
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